The tactile system is capable of discerning various textures, the distance to an object, shape, or even the velocity of a moving object. The versatility of this system is due to, at least partially, the heterogeneous population of mechanoreceptors that lies within the skin. In the whisker system this is achieved via three three types of mechanoreceptors (MR) in the whisker follicle: Slowly Adapting type 1 (SA1), Slowly Adapting type 2 (SA2), and Rapidly Adapting (RA) [1]. In this study, we aim to understand the role of these different types of MRs in stimulus representation. We fit Adaptive Exponential integrate and fire (Adex) neurons to the published MR data [1]. We integrate the lemniscal pathway of the whisker system, composed of the trigeminal ganglion (TG), the brainstem (BS), the thalamus (TC), and finally layer 4 of the barrel field of the somatosensory cortex (bfd) to MRs, resulting in a multi-layer computational network of the first four stages of the whisker-to-barrel circuit in whisking rodents [2]. We feed a ramp-and-hold stimulus that varies in direction (0-350 degrees), amplitude, and duration (10-200 ms) and compare it to the experimental data [3]. We train a linear decoder based on firing rate per neuron for each layer and assess the accuracy by comparing the prediction to the stimulus. All decoders are trained on the same number of trials. We find that the accuracy of the decoder saturates in TG, independently of the nature of the parameter that is changed (i.e. direction, amplitude, duration). We find that direction is encoded in all three types of MRs but represented best by SA1. Amplitude is poorly represented in individual MRs, even though SA2 outperforms other MRs. Finally, the duration of the stimulus is mostly encoded by the SA1 population, suggesting that MR populations encode complimentary stimulus features. The separated information is later combined in the TG, allowing optimal decodability of stimulus parameters, which is then transmitted along the ascending pathway without significant “information” loss. This model is, to the best of our knowledge, the first model of the ascending whisker-to-barrel cortex and will help to study the principles of information flow in the whisker system.